基于机器学习和生物信息学分析的脂肪酸代谢相关基因在牙周炎中的作用研究

doi:10.7518/hxkq.2024.2024214

华西口腔医学杂志 ›› 2024, Vol. 42 ›› Issue (6): 735-747.doi: 10.7518/hxkq.2024.2024214

基于机器学习和生物信息学分析的脂肪酸代谢相关基因在牙周炎中的作用研究

陈宇翔¹^,²(), 赵安娜¹^,², 杨浩然¹^,², 杨霞¹, 程婷婷¹^,², 饶先琦¹^,², 李自良¹^,²()

^1.昆明医科大学附属口腔医院，昆明 650000
^2.云南省口腔医学重点实验室，昆明 650000

收稿日期:2024-06-02 修回日期:2024-07-23 出版日期:2024-12-01 发布日期:2024-11-29
通讯作者: 李自良 E-mail:474482766@qq.com;1752114604@qq.com
作者简介:陈宇翔，硕士，E-mail：474482766@qq.com
基金资助:
国家自然科学基金(82360185)

Role of fatty acid metabolism-related genes in periodontitis based on machine learning and bioinformatics analysis

Chen Yuxiang¹^,²(), Zhao Anna¹^,², Yang Haoran¹^,², Yang Xia¹, Cheng Tingting¹^,², Rao Xianqi¹^,², Li Ziliang¹^,²()

^1.Stomatological Hospital of Kunming Medical University, Kunming 650000, China
^2.Yunnan Provincial Key Laboratory of Stomatology, Kunming 650000, China

Received:2024-06-02 Revised:2024-07-23 Online:2024-12-01 Published:2024-11-29
Contact: Li Ziliang E-mail:474482766@qq.com;1752114604@qq.com
Supported by:
National Natural Science Foundation of China(82360185)

摘要/Abstract

摘要：

目的通过机器学习和生物信息学方法研究脂肪酸代谢相关基因在牙周炎中的作用。方法从GEO数据库下载牙周炎数据集GSE10334和GSE16134，GeneCards数据库下载脂肪酸代谢相关基因集。通过R语言“limma”包筛选牙周炎中差异表达的脂肪酸代谢相关基因（DEFAMRGs），并进行功能富集和通路分析。进一步用递归特征消除、最小绝对收缩和选择算子和Boruta算法确定枢纽DEFAMRGs，并用其构建诊断模型且进行内部和外部验证。利用一致性聚类分析构建枢纽DEFAMRGs相关的牙周炎亚型。利用CIBERSORT软件分析牙龈组织的免疫细胞浸润，并探究枢纽DEFAMRGs和免疫细胞之间的相关性。结果共筛选出113个牙周炎DEFAMRGs。富集分析结果表明，DEFAMRGs主要和免疫炎症反应以及免疫细胞趋化相关。最终确定8个枢纽DEFAMRGs（BTG2、CXCL12、FABP4、CLDN10、PPBP、RGS1、LGALSL和RIF1）并构建了诊断模型（AUC=0.967），基于此将牙周炎分为两个亚型。此外，枢纽DEFAMRGs与不同免疫细胞群体之间存在显著的相关性，其中相关性较高的免疫细胞是肥大细胞和树突状细胞。结论该研究为牙周炎的发生发展机制提供新的见解和思路，基于枢纽DEFAMRGs构建的诊断模型可为牙周炎的诊断和治疗提供新的方向。

关键词: 牙周炎, 脂肪酸代谢, 生物信息学, 机器学习, 免疫浸润

Abstract:

Objective This study aims to investigate the role of genes related to fatty acid metabolism in periodontitis through machine learning and bioinformatics methods. Methods Periodontitis datasets GSE10334 and GSE-16134 were downloaded from the GEO database, and the fatty acid metabolism-related gene sets were obtained from the GeneCards database. Differentially expressed fatty acid metabolism-related genes (DEFAMRGs) in periodontitis were screened using the “limma” R package. Functional enrichment and pathway analyses were conducted. Recursive Feature Elimination, Least Absolute Shrinkage and Selection Operator, and Boruta algorithm were used to determine hub DEFAMRGs and construct diagnostic models with internal and external validation. Subtypes of periodontitis related to hub DEFAMRGs were constructed using consistency clustering analysis. CIBERSORT was used to analyze immune cell infiltration in gingival tissues and explore the correlation between hub DEFAMRGs and immune cells. Results A total of 113 periodontitis DEFAMRGs were screened out as a result. The enrichment analysis results indicate that DEFAMRGs are mainly associated with immune inflammatory responses and immune cell chemotaxis.Finally, 8 hub DEFAMRGs (BTG2, CXCL12, FABP4, CLDN10, PPBP, RGS1, LGALSL, and RIF1) were identified and a diagnostic model (AUC=0.967) was constructed, based on which periodontitis was divided into two subtypes. In addition, there is a significant correlation between hub DEFAMRGs and different immune cell populations, with mast cells and dendritic cells showing higher correlation. Conclusion This study provides new insights and ideas for the occurrence and development mechanism of periodontitis and proposes a diagnostic model based on hub DEFAMRGs to provide new directions for diagnosis and treatment.

Key words: periodontitis, fatty acid metabolism, bioinformatics, machine learning, immune infiltration

中图分类号:

R781.4

陈宇翔, 赵安娜, 杨浩然, 杨霞, 程婷婷, 饶先琦, 李自良. 基于机器学习和生物信息学分析的脂肪酸代谢相关基因在牙周炎中的作用研究[J]. 华西口腔医学杂志, 2024, 42(6): 735-747.

Chen Yuxiang, Zhao Anna, Yang Haoran, Yang Xia, Cheng Tingting, Rao Xianqi, Li Ziliang. Role of fatty acid metabolism-related genes in periodontitis based on machine learning and bioinformatics analysis[J]. West China Journal of Stomatology, 2024, 42(6): 735-747.

图/表 7

图 1

图 2

图 3

图 4

图 5

表 1

图 6

参考文献 56

1	Cui Y, Tian G, Li R, et al. Epidemiological and sociodemographic transitions of severe periodontitis incidence, prevalence, and disability-adjusted life years for 21 world regions and globally from 1990 to 2019: an age-period-cohort analysis[J]. J Periodontol, 2023, 94(2): 193-203.
2	Wen X, Li H, Li S, et al. Associated factors of periodontitis and predicted study among young man in China: a population-based cross-sectional study[J]. BMC Public Health, 2024, 24(1): 1235.
3	Lin J, Pei T, Yang H. Association between modifiable lifestyle pattern and periodontitis: a cross-sectional study ba-sed on NHANES[J]. BMC Oral Health, 2024, 24(1): 591.
4	Nepomuceno R, Vallerini BF, da Silva RL, et al. Syste-mic expression of genes related to inflammation and li-pid metabolism in patients with dyslipidemia, type 2 diabetes mellitus and chronic periodontitis[J]. Diabetes Me-tab Syndr, 2019, 13(4): 2715-2722.
5	Masoodi M, Kuda O, Rossmeisl M, et al. Lipid signaling in adipose tissue: connecting inflammation & metabolism[J]. Biochim Biophys Acta, 2015, 1851(4): 503-518.
6	Calder PC. Functional roles of fatty acids and their effects on human health[J]. JPEN J Parenter Enteral Nutr, 2015, 39(1 ): 18S-32S.
7	Ma WT, Zou ZL, Yang LS, et al. Exploring the bi-directional relationship between periodontitis and dyslipide-mia: a comprehensive systematic review and meta-analysis[J]. BMC Oral Health, 2024, 24(1): 508.
8	Salminen A, Määttä AM, Mäntylä P, et al. Systemic me-tabolic signatures of oral diseases[J]. J Dent Res, 2024, 103(1): 13-21.
9	Alves-Costa S, Leite FRM, Ladeira LLC, et al. Behavio-ral and metabolic risk factors associated with periodontitis in Brazil, 1990-2019: a multidimensional analysis for the Global Burden of Disease Study 2019[J]. Clin Oral Investig, 2023, 27(12): 7909-7917.
10	Bitencourt FV, Nascimento GG, Costa SA, et al. The role of dyslipidemia in periodontitis[J]. Nutrients, 2023, 15(2): 300.
11	Gomes-Filho IS, Freitas TOB, Cruz SSD, et al. Perio-dontitis in individuals with few remaining teeth and a high gingival bleeding index increases the probability of dyslipidemia[J]. J Periodontol, 2023, 94(10): 1243-1253.
12	Mirzaei A, Shahrestanaki E, Malmir H, et al. Association of periodontitis with lipid profile: an updated systematic review and meta-analysis[J]. J Diabetes Metab Disord, 2022, 21(2): 1377-1393.
13	Veljovic T, Djuric M, Mirnic J, et al. Lipid peroxidation levels in saliva and plasma of patients suffering from pe-riodontitis[J]. J Clin Med, 2022, 11(13): 3617.
14	Zhao Y, Zheng ZW, Zhang MH, et al. Design, synthesis, and evaluation of mono-carbonyl analogues of curcumin (MCACs) as potential antioxidants against periodontitis[J]. J Periodontal Res, 2021, 56(4): 656-666.
15	Sun Y, Yin YY, Yang SH, et al. Lipotoxicity: The mis-sing link between diabetes and periodontitis[J]. J Perio-dontal Res, 2024, 59(3): 431-445.
16	Mohideen K, Chandrasekar K, Ramsridhar S, et al. Assessment of oxidative stress by the estimation of lipid peroxidation marker malondialdehyde (MDA) in patients with chronic periodontitis: a systematic review and meta-analysis[J]. Int J Dent, 2023, 2023: 6014706.
17	Patil RT, Dhadse PV, Salian SS, et al. Role of oxidative stress in periodontal diseases[J]. Cureus, 2024, 16(5): e-60779. DOI:10.7759/cureus.60779 .
18	Li G, Robles S, Lu ZY, et al. Upregulation of free fatty acid receptors in periodontal tissues of patients with metabolic syndrome and periodontitis[J]. J Periodontal Res, 2019, 54(4): 356-363.
19	Soubiya, Madaiah H, Tarannum F, et al. Association of adipocyte fatty acid-binding protein and tumor necrosis factor alpha with periodontal health and disease: a cross-sectional investigation[J]. Dent Res J (Isfahan), 2021, 18: 79.
20	Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso[J]. Biostatistics, 2008, 9(3): 432-441.
21	Chen Q, Meng ZP, Liu XY, et al. Decision variants for the automatic determination of optimal feature subset in RF-RFE[J]. Genes (Basel), 2018, 9(6): 301.
22	Kursa MB, Rudnicki WR. Feature selection with the Boruta Package[J]. J Stat Soft, 2010, 36(11): 1-13.
23	Zhou X, Zhu X, Zeng H. Fatty acid metabolism in adaptive immunity[J]. Febs J, 2023, 290(3): 584-599.
24	Kokotou MG, Mantzourani C, Batsika CS, et al. Lipidomics analysis of free fatty acids in human plasma of healthy and diabetic subjects by liquid chromatography-high resolution mass spectrometry (LC-HRMS)[J]. Biomedicines, 2022, 10(5): 1189.
25	Kersten S. Angiopoietin-like 3 in lipoprotein metabolism[J]. Nat Rev Endocrinol, 2017, 13(12): 731-739.
26	Ni Y, Zhao LJ, Yu HY, et al. Circulating unsaturated fatty acids delineate the metabolic status of obese indivi-duals[J]. EBioMedicine, 2015, 2(10): 1513-1522.
27	Rocha DM, Bressan J, Hermsdorff HH. The role of die-tary fatty acid intake in inflammatory gene expression: a critical review[J]. Revista Paulista De Med, 2017, 135(2): 157-168.
28	Das UN. Essential fatty acids and their metabolites in the pathobiology of inflammation and its resolution[J]. Biomolecules, 2021, 11(12): 1873.
29	Kim SH, Jung IR, Hwang SS. Emerging role of anti-proliferative protein BTG1 and BTG2[J]. BMB Rep, 2022, 55(8): 380-388.
30	Li Z, Fu R, Wen X, et al. Network analysis reveals miRNA crosstalk between periodontitis and oral squamous cell carcinoma[J]. BMC Oral Health, 2023, 23(1): 19.
31	Mahanonda R, Champaiboon C, Subbalekha K, et al. Human memory B cells in healthy gingiva, gingivitis, and periodontitis[J]. J Immunol, 2016, 197(3): 715-725.
32	Kim DJ, Rho JH, Woo BH, et al. Periodontal pathogens modulate lipid flux via fatty acid binding protein 4[J]. J Dent Res, 2019, 98(13): 1511-1520.
33	Li X, Tse HF, Yiu KH, et al. Periodontal therapy decrea-ses serum levels of adipocyte fatty acid-binding protein in systemically healthy subjects: a pilot clinical trial[J]. J Periodontal Res, 2013, 48(3): 308-314.
34	Hadj-Rabia S, Brideau G, Al-Sarraj Y, et al. Multiplex epithelium dysfunction due to CLDN10 mutation: the HELIX syndrome[J]. Genet Med, 2018, 20(2): 190-201.
35	Zeng WJ, Liu JR, Ouyang XY, et al. The expression le-vels of chemotaxis-related molecules CXC chemokine receptor 1, interleukin-8, and pro-platelet basic protein in gingival tissues[J]. J Dent Sci, 2024, 19(1): 58-63.
36	Moratz C, Harrison K, Kehrl JH. Regulation of chemokine-induced lymphocyte migration by RGS proteins[J]. Methods Enzymol, 2004, 389: 15-32.
37	Hara A, Niwa M, Noguchi K, et al. Galectin-3 as a next-generation biomarker for detecting early stage of various diseases[J]. Biomolecules, 2020, 10(3): 389.
38	Kong NN, Liu ZY, Chan YW. RIF₁ suppresses the formation of single-stranded ultrafine anaphase bridges via protein phosphatase 1[J]. Cell Rep, 2023, 42(2): 112032.
39	McLachlan GJ. Cluster analysis and related techniques in medical research[J]. Stat Methods Med Res, 1992, 1(1): 27-48.
40	Han NN, Liu YT, Du J, et al. Regulation of the host immune microenvironment in periodontitis and periodontal bone remodeling[J]. Int J Mol Sci, 2023, 24(4): 3158.
41	Kimura K, Kitaura H, Fujii T, et al. Anti-c-Fms antibody inhibits lipopolysaccharide-induced osteoclastogenesis in vivo [J]. FEMS Immunol Med Microbiol, 2012, 64(2): 219-227.
42	Huang HL, Long LY, Zhou PP, et al. mTOR signaling at the crossroads of environmental signals and T-cell fate decisions[J]. Immunol Rev, 2020, 295(1): 15-38.
43	Garlet GP, Sfeir CS, Little SR. Restoring host-microbe homeostasis via selective chemoattraction of Tregs[J]. J Dent Res, 2014, 93(9): 834-839.
44	Balmert SC, Donahue C, Vu JR, et al. In vivo induction of regulatory T cells promotes allergen tolerance and suppresses allergic contact dermatitis[J]. J Control Release, 2017, 261: 223-233.
45	Kadomoto S, Izumi K, Mizokami A. Macrophage polarity and disease control[J]. Int J Mol Sci, 2021, 23(1): 144.
46	Alvarez C, Rojas C, Rojas L, et al. Regulatory T lymphocytes in periodontitis: a translational view[J]. Mediators Inflamm, 2018, 2018: 7806912.
47	Seidel A, Seidel CL, Weider M, et al. Influence of natural killer cells and natural killer T cells on periodontal disease: a systematic review of the current literature[J]. Int J Mol Sci, 2020, 21(24): 9766.
48	Wilensky A, Chaushu S, Shapira L. The role of natural killer cells in periodontitis[J]. Periodontol 2000, 2015, 69(1): 128-141.
49	Wang HY, Wei RQ, Deng TJ, et al. Identifying immuno-related diagnostic genes and immune infiltration signatures for periodontitis and alopecia areata[J]. Int Immunopharmacol, 2023, 124(Pt B): 110880.
50	Rathod S, Raj A, Wanikar I. Quantitative analysis of mast cell count and density in chronic periodontal disea-se[J]. J Indian Soc Periodontol, 2018, 22(2): 107-111.
51	Tetè G, D'orto B, Ferrante L, et al. Role of mast cells in oral inflammation[J]. J Biol Regul Homeost Agents, 2021, 35(4 ): 65-70.
52	Tada H, Nishioka T, Takase A, et al. Porphyromonas gingivalis induces the production of interleukin-31 by human mast cells, resulting in dysfunction of the gingival epithelial barrier[J]. Cell Microbiol, 2019, 21(3): e12972. DOI:10.1111/cmi.12972 .
53	Ribeiro LSFE, dos Santos JN, Rocha CAG, et al. Asso-ciation between mast cells and collagen maturation in chronic periodontitis in humans[J]. J Histochem Cytochem, 2018, 66(6): 467-475.
54	El-Awady AR, Elashiry M, Morandini AC, et al. Dendritic cells a critical link to alveolar bone loss and systemic disease risk in periodontitis: immunotherapeutic implications[J]. Periodontol 2000, 2022, 89(1): 41-50.
55	Sharawi H, Heyman O, Mizraji G, et al. The prevalence of gingival dendritic cell subsets in periodontal patients[J]. J Dent Res, 2021, 100(12): 1330-1336.
56	Elsayed R, Elashiry M, Liu YT, et al. Microbially-induced exosomes from dendritic cells promote paracrine immune senescence: novel mechanism of bone degenerative disease in mice[J]. Aging Dis, 2023, 14(1): 136-151.

类别	亚型1（n=170）	亚型1（n=77）	P值
BTG2	10.344（10.057，10.832）	9.147（8.757，9.560）	<0.001
CXCL12	10.093（9.800，10.394）	9.126（8.575，9.608）	<0.001
FABP4	7.536（6.981，8.372）	6.044（5.629，6.592）	<0.001
CLDN10	6.154（5.497，6.585）	4.891（4.508，5.398）	<0.001
PPBP	5.557（4.867，6.270）	4.333（4.098，4.862）	<0.001
RGS1	9.988（9.383，10.648）	8.420（7.592，9.072）	<0.001
LGALSL	10.642（10.132，11.015）	11.572（11.24，11.890）	<0.001
RIF1	6.283（5.710，6.684）	7.228（6.673，7.739）	<0.001
牙周状态［n（%）］			<0.001
健康	8（4.7）	56（72.7）
牙周炎	162（95.3）	21（27.3）

[1]	毛莉艳, 杨茜茜, 毕小琴, 刘敏, 赵重阳, 温作珍. 口腔癌患者失志综合征风险预测模型构建及验证[J]. 华西口腔医学杂志, 2025, 43(3): 395-405.
[2]	张雪颖, 孟鑫, 刘志臻, 张康, 冀洪海, 孙敏敏. 人参皂苷Rb3调节磷酸化细胞外信号调节激酶通路减轻牙周炎大鼠炎症反应促进成骨[J]. 华西口腔医学杂志, 2025, 43(2): 236-248.
[3]	冯苗苗, 徐小苒, 李宁丽, 杨铭真, 翟远坤. 基于网络药理、分子对接和分子动力学模拟探讨蛇床子治疗牙周炎伴骨质疏松的作用机制[J]. 华西口腔医学杂志, 2025, 43(2): 249-261.
[4]	付岚清, 郝新宇, 钱文博, 孙颖. 基础治疗对重度牙周炎患者龈沟液内中性粒细胞胞外诱捕网形成的影响研究[J]. 华西口腔医学杂志, 2025, 43(1): 46-52.
[5]	周露露, 滕念, 高甜甜, 王洪斌, 高翔. 香芹酚水凝胶对牙周炎大鼠牙槽骨保护作用研究[J]. 华西口腔医学杂志, 2024, 42(5): 593-608.
[6]	李琼, 马浩楠, 商雅琦, 辛禧瑞, 刘歆婵, 武洲, 于维先. 线粒体解偶联蛋白2在大鼠实验性牙周炎相关肾损伤中的作用研究[J]. 华西口腔医学杂志, 2024, 42(4): 502-511.
[7]	杨荣霞, 宗颖睿, 张晨. 慢性牙周炎与帕金森病之间潜在相关性的初探[J]. 华西口腔医学杂志, 2024, 42(4): 521-530.
[8]	李钺, 许春梅, 谢旭东, 施培磊, 王骏, 丁一. 骨膜蛋白在小鼠牙周炎进程中的时空表达规律研究[J]. 华西口腔医学杂志, 2024, 42(3): 286-295.
[9]	辛雨, 傅若冰, 辛禧瑞, 商雅琦, 刘歆婵, 于维先. 缝隙连接蛋白43通过调控凋亡参与大鼠牙周炎相关肾损伤[J]. 华西口腔医学杂志, 2024, 42(3): 296-303.
[10]	马浩楠, 李琼, 商雅琦, 辛禧瑞, 刘歆婵, 武洲, 于维先. 生物钟蛋白Bmal1对实验性牙周炎相关肾损伤的影响[J]. 华西口腔医学杂志, 2024, 42(2): 163-171.
[11]	孙金梦, 张颖, 郑泽君, 丁晓玲, 孙敏敏, 丁刚. 基于网络药理学和分子对接技术探讨人参对牙周炎的潜在治疗机制[J]. 华西口腔医学杂志, 2024, 42(2): 181-191.
[12]	杨浩然, 陈宇翔, 赵安娜, 程婷婷, 周建忠, 李自良. 基于随机森林和人工神经网络构建种植体周炎的诊断模型[J]. 华西口腔医学杂志, 2024, 42(2): 214-226.
[13]	叶畅畅, 杨禾, 黄萍. 意向性牙再植术保留重度牙周炎患牙的临床应用策略[J]. 华西口腔医学杂志, 2024, 42(1): 12-18.
[14]	王勤涛, 马志伟, 王津津. 重度牙周炎患牙拔除或挽救之思考[J]. 华西口腔医学杂志, 2023, 41(6): 635-640.
[15]	张彦表, 魏美容, 夏天永, 尹文婷, 毛淑梅. 2型糖尿病患者血清半乳糖凝聚素-3水平与牙周炎的相关性研究[J]. 华西口腔医学杂志, 2023, 41(6): 653-661.

基于机器学习和生物信息学分析的脂肪酸代谢相关基因在牙周炎中的作用研究

Role of fatty acid metabolism-related genes in periodontitis based on machine learning and bioinformatics analysis

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 56

相关文章 15

编辑推荐

Metrics

本文评价